Data sets with temporal dependencies frequently occur in many business, engineering and scientific scenarios. Some typical examples of temporal data include alarm sequences in a telecom network, transaction logs in a grocery store, web navigation history, genome sequence data, stock market or other financial transactions data, line status logs in a manufacturing plant or other log data from manufacturing systems, time-stamped warranty data, diagnostic data from automobiles, and customer relations data.
The widespread occurrence of temporal data series has brought attention to the general importance of the area of temporal data mining. One way to search for patterns of interest in time series data is to discover frequent (or repetitive) patterns in the data. Thus, a special class of temporal data mining applications, those having to do with frequent episodes, is of particular importance. A central idea of frequent episode discovery is to seek expressive pattern structures and fast discovery algorithms that render a discovery technique both useful as well as efficient in the data-mining context.
In many event sequences, individual time-ordered events in the data series are associated with time durations. In many instances, the time durations may carry useful information. For example, in the line status logs of manufacturing plants, the durations of various events in the data stream carry important information. Accordingly, formalisms that can accommodate time durations while searching for interesting temporal patterns in the data would be very useful.